Industrial Engineering Workflow Automation

We’ve called efficiency the unsung hero of the energy transition in the past. While the energy transition will happen first through the transition of energy usages, like the shift with transport, from internal combustion engines to electric vehicles, or from fuel or gas boilers to heat pumps, we cannot ignore the utmost priority of the energy transition: efficiency. Efficiency is the greatest path to reduce our energy use, our impact on the world’s climate through CO2 emission reduction, and very importantly, the best way to make solid and practical savings. In its most historical form, energy efficiency is about better insulation, to reduce heating (or cooling) loss in buildings like family homes, warehouses, office high rises, and shopping malls. This is useful, but expensive and tedious to realize on existing installations. Digitizing home, buildings, industries and infrastructure brings similar benefits at a much lower cost and a much higher economic return. The combination of IoT, big data, software and AI can significantly reduce energy use and waste by detecting leaky valves, or automatically adjusting heating, lighting, processes and other systems to the number of people present at any given time, using real-time data analysis. It also allows owners to measure precisely progress, report automatically on their energy and sustainability parameters, and benefit from new services through smart grid interaction. And this is just the energy benefit. Automation and digital tools also optimize the processes, safety, reliability, and uptime leading to greater productivity and performance.

Securing the Invisible: Cybersecurity Challenges in Smart Manufacturing Last year, a European automotive plant faced a production halt that lasted nearly a week. The cause was not a broken robot arm but a ransomware attack that locked the SCADA servers running the assembly line. The impact rippled through suppliers, deliveries, and customer orders. This was a wake-up call: in the era of smart manufacturing, cyber risk is no longer an IT problem, it is an operational crisis. Factories are undergoing a deep transformation. Industrial Internet of Things, digital twins, predictive maintenance, and AI-driven analytics promise efficiency. Yet every new PLC, sensor, and cloud interface expands the attack surface. Unlike IT networks, plants run 24/7 with minimal tolerance for downtime. A single compromised controller can halt production, with losses climbing by the hour. The convergence of IT and OT makes this more complex. IT can be patched weekly, but many OT devices run legacy firmware untouched for years because a reboot may interrupt production. This asymmetry is exploited by attackers who move laterally from corporate systems into plant floors, abusing outdated protocols and weak segmentation. Standards are beginning to address these gaps. IEC 62443 promotes defense-in-depth through zoning and conduits that isolate control networks from enterprise IT. NIS2 in Europe forces essential manufacturers to strengthen resilience and report incidents. ISO 27001, traditionally IT-focused, is increasingly combined with OT frameworks to unify governance and compliance. The response cannot be purely technical. Zero Trust principles are reaching the factory floor, where strict access control applies even to engineers connecting remotely. Security operation centers are learning to monitor not only servers but also industrial traffic. More importantly, boards now understand that downtime caused by a cyberattack is a financial event with direct impact on revenue and reputation. The future of smart factories depends on building resilience as much as efficiency. Cybersecurity is no longer an afterthought but a design principle. Every connected device is both a source of data and a potential entry point. The companies embedding security into production systems today will not only avoid shutdowns but also secure their place in tomorrow’s global supply chain. References • IEC 62443 Industrial Security Standards – https://lnkd.in/dFtHdHAk • EU NIS2 Directive Overview – https://lnkd.in/dfexNjUn • ISO/IEC 27001 Information Security – https://lnkd.in/dtRG_ntE #OTsecurity #SmartManufacturing #IEC62443 #NIS2 #ZeroTrust #Industry40 #CyberResilience #SCADA #IIoT

It is not enough to make existing software and enterprise workflows “AI-enhanced”, they need to be fully rethought. Too many companies are slapping AI onto outdated systems like it’s a plugin. Hoping for exponential results with incremental changes. But real transformation doesn't come from enhancement. It comes from FULL REINVENTION. Here’s what that looks like: 1/ Rethinking workflows from first principles, not just adding chatbots or automating some steps, but redesigning processes entirely around AI’s strengths. 2/ Rebuilding software around intelligence, not interfaces, AI should be the core engine, not a helper bolted on the side. 3/ Reimagining roles and collaboration, letting humans focus on strategy, creativity, and judgment while AI handles the grind. AI isn’t an upgrade. It’s a paradigm shift.

🚨 𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐓𝐡𝐫𝐞𝐚𝐭𝐬 𝐭𝐨 𝐀𝐈 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 𝐭𝐨 𝐊𝐧𝐨𝐰 🚨 Imagine your AI system making decisions based on data that's been subtly tampered with. Sounds like science fiction? Think again. Security researcher 𝐽𝑜ℎ𝑎𝑛𝑛 𝑅𝑒ℎ𝑏𝑒𝑟𝑔𝑒𝑟 recently uncovered vulnerabilities in AI models like ChatGPT that could allow malicious actors to inject harmful instructions and extract sensitive data over time. As AI becomes integral to our decision-making processes, we have to ask: 𝐇𝐨𝐰 𝐬𝐞𝐜𝐮𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞𝐬𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐬𝐭𝐞𝐩𝐬 𝐜𝐚𝐧 𝐰𝐞 𝐭𝐚𝐤𝐞 𝐭𝐨 𝐩𝐫𝐨𝐭𝐞𝐜𝐭 𝐭𝐡𝐞𝐦? 🔍 𝐓𝐡𝐞 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞: 🛑 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐢𝐬𝐤𝐬: AI models are susceptible to adversarial inputs- malicious data crafted to deceive or influence system outputs. 🕵️♂️ 𝐒𝐢𝐥𝐞𝐧𝐭 𝐄𝐱𝐩𝐥𝐨𝐢𝐭𝐚𝐭𝐢𝐨𝐧: Attackers might manipulate AI behavior or siphon off confidential information without immediate detection. 🔒 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: Firewalls and standard cybersecurity measures aren't enough. We need strategies that ensure AI systems process and learn from trustworthy data. 🤔 𝐏𝐨𝐢𝐧𝐭𝐬 𝐭𝐨 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫: 🔓 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲 𝐯𝐬. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: How do we balance the openness that fosters AI innovation with the need to protect against exploitation? 🤝 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: What roles do developers, organizations, and users play in safeguarding AI systems? 🚀 𝐅𝐮𝐭𝐮𝐫𝐞 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: If AI can be manipulated today, what does this mean for more advanced systems tomorrow? 🔑 𝐖𝐡𝐚𝐭 𝐂𝐚𝐧 𝐖𝐞 𝐃𝐨? 📖 𝐒𝐭𝐚𝐲 𝐈𝐧𝐟𝐨𝐫𝐦𝐞𝐝: Keep abreast of the latest developments in AI security to understand potential vulnerabilities. 🛠️ 𝐏𝐫𝐨𝐦𝐨𝐭𝐞 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬: Encourage the adoption of secure coding practices and regular audits in AI development. 🤝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 𝐨𝐧 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: Work with industry peers, cybersecurity experts, and policymakers to develop robust defense mechanisms. In a world where AI influences everything from business strategies to personal recommendations, ensuring the integrity of these systems is paramount. 𝐂𝐚𝐧 𝐰𝐞 𝐚𝐟𝐟𝐨𝐫𝐝 𝐭𝐨 𝐨𝐯𝐞𝐫𝐥𝐨𝐨𝐤 𝐭𝐡𝐞 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐯𝐞𝐫𝐲 𝐭𝐨𝐨𝐥𝐬 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐨𝐮𝐫 𝐟𝐮𝐭𝐮𝐫𝐞? 💬 𝐋𝐞𝐭'𝐬 𝐬𝐭𝐚𝐫𝐭 𝐚 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧! What measures do you believe are essential in securing AI against emerging threats? Share your thoughts below! 🔽 🔗 Link to Johann Rehberger's analysis: https://lnkd.in/d9QVwE_5 #AI #Cybersecurity #DataIntegrity #FutureTech #Collaboration #AIEthics ¦ Deloitte

How to make a super automated module product line？ First, high-quality and highly automated machines are the basics. Take the cell tabbing and stringing machine as an example, Canadian Solar Inc. is the first one in the industry to bring half-cell and multiple busbar tech into the mass production. After seven years ‘development, Canadian Solar increased the soldering speed by about 3 times and lowered the defect rate by 50% when wafers are thinned by more than 70%. Second, we leverage #AI to do what they do best - image and video analysis, defect identification and root cause analysis. We started to use neural networks to find defects in EL inspection images as early in 2018. Now, all EL and appearance defect identification and analysis are done by AI at our automated lines, greatly improve the efficiency and quality of this highly repetitive work. Third, we use conveyor lines to transport products at work and automated guided vehicles (AGVs) to transport materials. There is no need for people to do the lifting and transportation work any longer, which reduces the labor intensity significantly. Fourth, an information system enabling the info flow from customers and material suppliers to the production lines is essential. Our info system connects customer relationship management (CRM), supplier relationship management (SRM), enterprise resource planning (ERP) and manufacturing execution system (MES). Highly personalized requests from customers can be implemented on automated production lines flawlessly.  Last by not least, we have a dedicated and experienced team to run the lines. In the era of artificial intelligence, people are still the core, which is Canadian Solar’s irreplaceable asset. This team has increased production efficiency fourfold since we first introduced half-cell and muti-busbar automated module line seven years ago. I am proud of them and believe they will bring more progress to the industry in the future.  #automation #automanufacture #solar #autoproduction

"AI is going to do to knowledge work what Lean did to manufacturing." – Satya Nadella This quote has been stuck in my head since I first heard it. The more I think about it, the clearer it becomes: Lean eliminated waste, optimized workflows, and empowered workers to operate at a higher level. AI is doing the same for knowledge work, not by replacing people, but by shifting their focus to higher-impact tasks. Here’s where the parallels stand out to me—and why I think you should pay attention: ✅ Eliminating Waste - Lean cut unnecessary inventory and idle time. AI is removing repetitive knowledge tasks that were once too unstructured to automate. ➡️ Instant meeting summaries ➡️ Automated data entry ➡️ Seamless report generation 🤔 What changes? Roles shift. Companies will need to redefine job responsibilities, redeploy talent, and rethink required skills. ✅ Just-in-Time Insights - Lean production meant the right materials, at the right time. AI can deliver insights exactly when needed. ➡️ No more waiting for monthly reports—benchmarking happens instantly. ➡️ Marketing teams can approve AI-recommended campaign updates in real time. 🤔 What changes? Decision-making accelerates. Companies will need flatter org structures and leaders who are comfortable with continuous iteration instead of rigid planning cycles. ✅ Continuous Improvement - Lean championed small, ongoing improvements. AI now enables continuous, real-time enhancements. ➡️ Writers get instant clarity recommendations. ➡️ Sales teams receive AI-driven coaching on the fly. ➡️ Customer interactions improve through proactive suggestions. 🤔 What changes? A culture of experimentation becomes essential. Companies that reward iteration and learning will move faster than those that don’t. ✅ Empowered Workers- Lean gave factory workers more control over processes. AI is doing the same for knowledge workers by equipping them with expert-level insights and decision-making capability. ➡️ Customer support reps can resolve complex issues without escalation. ➡️ Employees make better, faster decisions without waiting for approvals. 🤔 What changes? Employee expectations shift. More autonomy means leaders must focus on coaching over command-and-control management. We’re at the start of a major transformation. At Dropbox, we’re building Dash to help knowledge workers focus on high-impact work, not busywork. And tomorrow, I'm giving a talk at the Gartner symposium in Dallas to share what we've learned tackling these challenges head on. Which Lean principles feel most relevant to how AI is changing your work? Let me know down below (and if you're in Dallas, come say hi!).

Navigating the Aftermath: Managing an AI-Powered Railway Post-Cyber Attack As artificial intelligence (AI) becomes the backbone of modern railway systems—optimizing routes, predicting maintenance, and enhancing safety—cyber threats have grown exponentially. A single attack can paralyze operations, disrupt schedules, and compromise passenger safety. Over the past five years, cyber incidents targeting railways have surged by over 220%, with cases like remote hijacking via radio frequencies in Poland (2023) and ticketing disruptions in Ukraine (2025) serving as stark reminders. Here’s a practical framework for managing an AI-driven railway system after a cyber attack. 1️⃣ Immediate Containment – Isolate and Assess Once an intrusion is detected, the first step is to contain it. In AI-managed railways, this means isolating compromised systems—dispatch algorithms, predictive maintenance modules, or signaling networks—from the rest. Activate a Rapid Response Team: Bring together cybersecurity experts, AI engineers, and railway operations specialists to identify attack vectors—whether phishing, ransomware, or signaling manipulation. Eradicate the Threat: Reset credentials, patch vulnerabilities, and enforce multi-factor authentication (MFA). For AI systems, encrypt models during storage and transmission to prevent theft or tampering.
The 2023 Polish incident, where 20 trains were halted via radio interference, proved how swift isolation minimizes damage. 2️⃣ Recovery & Restoration – Rebuild with Resilience Containment alone isn’t enough; recovery demands validating both physical assets and AI model integrity. System Integrity Checks: Apply frameworks such as NIST CSF 2.0 to verify that automated safety functions are uncompromised before resuming operations. Data Recovery: Restore from secure, encrypted backups; implement zero-trust access policies. Business Continuity: Test disaster-recovery plans regularly, ensuring seamless switchovers to manual operations when required.
Post-incident analysis should be mandatory—review logs, trace root causes, and update security policies, as seen in U.S. freight rail guidelines. 3️⃣ Long-Term Prevention – Fortify the Future True resilience lies in learning from the breach and preventing recurrences. Secure-by-Design: Embed cybersecurity through the AI lifecycle, from data collection to deployment. Continuous Monitoring: Use AI itself for real-time threat detection and anomaly analysis, ensuring human oversight in decision loops. Collaborate & Comply: Follow rail-specific cybersecurity standards and share threat intelligence across the ecosystem. AI can be both the target and the shield—its predictive power can detect attacks faster than humans ever could, provided its training data and parameters remain uncompromised. #CyberSecurity #AIRailway #InfrastructureManagement #Resilience #RailSafety #AIinTransport #CriticalInfrastructure

What happens when smart factories worldwide need to share real-time data with cloud-based business applications across the globe? #BMW tackled this by using #ApacheKafka for real-time data streaming from edge to cloud—connecting PLCs, robots, and IoT devices directly to #MES and #ERP systems. This enables powerful automation, predictive maintenance, and just-in-time logistics at global scale. In a smart factory scenario, #IoT and #edge computing meet #cloudnative data platforms. Confluent Cloud acts as the event backbone—decoupling physical systems from business logic and enabling real-time analytics, automation, and machine learning. This is not just theory. BMW Group streams production and logistics data from dozens of global plants into a Kafka-powered cloud infrastructure. Business units across the company benefit from the same live data streams for analytics, visibility, and decision-making. I recorded a 5-minute lightboard video that shows how data streaming connects smart factories and cloud-native architectures. Check it out and let me know what use cases you’re solving with #DataStreaming in #manufacturing or other edge-to-cloud scenarios. https://lnkd.in/eNwEtJE3 Where do you see the biggest opportunity for streaming data from edge to cloud—in logistics, predictive maintenance, or cross-site visibility? #SmartFactory #Industry40 #DigitalTwin #OPCUA #KafkaConnect #ConfluentCloud #ManufacturingInnovation

Virtual reality is not just a tool for entertainment but a game-changer in product design, allowing teams to experiment, refine, and collaborate remotely in ways that were once impossible, leading to faster innovation, cost reductions, and more precise manufacturing outcomes. Immersive 3D environments are transforming product design by eliminating physical constraints and allowing real-time iteration. Virtual prototyping enables companies to test designs without manufacturing costly models, reducing waste and accelerating development. Interactive visualization helps engineers refine products before production, leading to better ergonomics and functionality. Remote collaboration means teams across continents can work seamlessly, breaking traditional logistical barriers. Realistic product previews enhance customer trust and decision-making, particularly in industries like architecture, automotive, and consumer electronics, where accurate representations are crucial for investments and sales. #VirtualReality #3DDesign #ProductDevelopment #RemoteCollaboration #DigitalTransformation

🔷 Day 15: AI for Energy Efficiency in 5G (Green Networks) Building sustainable telecom networks through intelligent optimization. 5G Physical Layer Course link : https://lnkd.in/gnj4PtAZ 📌 The Energy Challenge in 5G 5G requires 10× more energy than LTE due to dense deployments, massive MIMO, and 24x7 connectivity RAN consumes over 70% of total network power Without optimization, energy costs and carbon footprints rise sharply 📌 How AI Makes 5G Greener 1. AI-Driven RAN Sleep Modes Detects low-traffic periods in real time Puts gNBs or antennas into sleep mode dynamically Ensures no impact on user QoS during transitions 2. Traffic Forecasting using ML Predicts network usage by area, time, and app Enables proactive energy provisioning Models: LSTM, Prophet, Gaussian Process Regression 3. Power-Aware Scheduling Optimizes PRB allocation considering SINR and energy trade-offs Reinforcement Learning for selecting energy-efficient resource maps 4. Adaptive Beamforming with AI Activates antenna panels only where users are present Reduces unnecessary radiation and consumption 5. Intelligent Cooling Systems AI manages hardware cooling based on real-time thermal load Cuts infrastructure energy use by up to 30% in data centers 📌 Use Cases Discussed AI shutting down small cells at midnight in suburban zones Dynamic TDD slot reconfiguration for energy-optimized uplink Edge inference on O-RAN SMO for green orchestration #5G #EnergyEfficiency #GreenNetworks #AIfor5G #NitinGupta #5GTraining #WhatsAppLearning #TelecomSustainability #O_RAN #Day15 #AIOptimization #NetworkEnergySaving #CarbonNeutral5G